# Pattern Analysis Computation Methods and Algorithms for Machine Learning

In machine learning, pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is “spam” or “non-spam”). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence.
Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to do “fuzzy” matching of inputs. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output to the sort provided by pattern-recognition algorithms.

Pattern Analysis Computation Methods:

• Ridge regression
• Regularized Fisher discriminant
• Regularized kernel Fisher discriminant
• Maximizing variance
• Maximizing covariance
• Canonical correlation analysis
• Kernel CCA
• Regularized CCA
• Kernel regularized CCA
• Smallest enclosing hyper sphere
• Soft minimal hyper sphere
• nu-soft minimal hyper sphere
• Hard margin SVM
• 1-norm soft margin SVM
• 2-norm soft margin SVM
• Ridge regression optimization
• Linear e-insensitive SVR
• nu-SVR
• Soft ranking
• Cluster quality
• Cluster optimization strategy
• Multiclass clustering
• Relaxed multiclass clustering
• Visualization quality

Pattern Analysis Algorithms:

• Normalization
• Centering data
• Simple novelty detection
• Parzen based classifier
• Cholesky decomposition or dual Gram�Schmidt
• Standardizing data
• Kernel Fisher discriminant
• Primal PCA
• Kernel PCA
• Whitening
• Primal CCA
• Kernel CCA
• Principal components regression
• PLS feature extraction
• Primal PLS
• Kernel PLS
• Smallest hyper sphere enclosing data
• Soft hyper sphere minimization
• nu-soft minimal hyper sphere
• Hard margin SVM
• Alternative hard margin SVM
• 1-norm soft margin SVM
• nu-SVM
• 2-norm soft margin SVM
• Kernel ridge regression
• 2-norm SVR
• 1-norm SVR
• nu-support vector regression
• Kernel perceptron
• On-line SVR
• nu-ranking
• On-line ranking
• Kernel k-means
• MDS for kernel-embedded data
• Data visualization

Source: http://www.kernel-methods.net/algos.html

Keywords: Data Mining, Machine Learning, Algorithms